WO2022158066A1 - Dispositif d'optimisation de conditions de production, programme et procédé d'optimisation de conditions de production - Google Patents

Dispositif d'optimisation de conditions de production, programme et procédé d'optimisation de conditions de production Download PDF

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WO2022158066A1
WO2022158066A1 PCT/JP2021/039329 JP2021039329W WO2022158066A1 WO 2022158066 A1 WO2022158066 A1 WO 2022158066A1 JP 2021039329 W JP2021039329 W JP 2021039329W WO 2022158066 A1 WO2022158066 A1 WO 2022158066A1
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quality
manufacturing
product
change
amount
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PCT/JP2021/039329
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English (en)
Japanese (ja)
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賢一 嘉手納
隆史 鎌田
敢 久光
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コニカミノルタ株式会社
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Priority to JP2022576979A priority Critical patent/JPWO2022158066A1/ja
Priority to CN202180090647.6A priority patent/CN116806329A/zh
Priority to US18/261,314 priority patent/US20240077854A1/en
Publication of WO2022158066A1 publication Critical patent/WO2022158066A1/fr

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    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B19/00—Programme-control systems
    • G05B19/02—Programme-control systems electric
    • G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS], computer integrated manufacturing [CIM]
    • G05B19/41875—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS], computer integrated manufacturing [CIM] characterised by quality surveillance of production
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B19/00—Programme-control systems
    • G05B19/02—Programme-control systems electric
    • G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS], computer integrated manufacturing [CIM]
    • G05B19/41865—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS], computer integrated manufacturing [CIM] characterised by job scheduling, process planning, material flow
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B19/00—Programme-control systems
    • G05B19/02—Programme-control systems electric
    • G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS], computer integrated manufacturing [CIM]
    • G05B19/41885—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS], computer integrated manufacturing [CIM] characterised by modeling, simulation of the manufacturing system
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/04—Manufacturing
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B2219/00—Program-control systems
    • G05B2219/30—Nc systems
    • G05B2219/31—From computer integrated manufacturing till monitoring
    • G05B2219/31372—Mes manufacturing execution system
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B2219/00—Program-control systems
    • G05B2219/30—Nc systems
    • G05B2219/32—Operator till task planning
    • G05B2219/32194—Quality prediction
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/30—Computing systems specially adapted for manufacturing

Definitions

  • the present invention relates to a manufacturing condition optimization device, a program, and a manufacturing condition optimization method for optimizing manufacturing conditions in manufacturing equipment.
  • the plant operating condition optimization system described in Patent Document 1 includes operating state data acquisition means for acquiring operating state data indicating the operating state of the plant measured by a plurality of sensors, and sensors provided in the plant. an operation index data acquisition unit for acquiring operation index data for evaluating plant operation, which is obtained based on the operation state data acquired by the operation state data acquisition unit; and the operation state data acquisition unit.
  • measurement data recording means for recording the measured data in a data recording unit by associating the obtained driving state data and the driving index data obtained by the driving index data acquisition means with each other based on a predetermined item as a set of measurement data; Then, based on the plurality of sets of measurement data recorded in the data recording unit, a predetermined multivariate analysis is performed using the driving state variables representing the driving state data side as explanatory variables and the driving index variables representing the driving index data side as objective variables.
  • a regression model creating means for creating a regression model by performing and an inverse transforming means for estimating explanatory variables from the components by a method corresponding to the component transforming means, which is created by the regression model creating means
  • Driving indicator variable optimization means for obtaining driving state variables for optimizing driving indicator variables based on a regression model, the purpose predicted by the predicting means while satisfying constraints on explanatory variables estimated by the inverse transforming means. It is characterized by obtaining explanatory variable values when optimizing an evaluation function relating to variables, and using the explanatory variable values as the optimum operating conditions.
  • the product quality depends on the manufacturing conditions of the product. Even if the proper manufacturing conditions are set, the quality may change due to disturbances that are difficult to control completely, such as temperature and material viscosity, and the yield of the product may decrease. The same is true for disturbances that are not measured or monitored in the manufacturing environment. Therefore, it is required to adjust manufacturing conditions in real time during manufacturing of products.
  • the operating condition optimization system described in Patent Literature 1 does not anticipate changes due to disturbances and adjust manufacturing conditions in real time. For this reason, optimization corresponding to uncontrollable changes in disturbance cannot be performed.
  • the present invention has been made in view of such a background, and is a manufacturing condition optimization apparatus, program, and manufacturing method that enable improvement of the manufacturing index by adjusting the manufacturing conditions of the product even when there is a disturbance.
  • An object of the present invention is to provide a condition optimization method.
  • (2) further comprising a quality variation estimation unit for estimating a variation in the quality of the product manufactured by the manufacturing equipment from the variation in the manufacturing conditions, wherein the yield estimation unit is configured to: estimating the probability that the product will pass the inspection by the inspection equipment from the estimated amount of change in the quality of the product, and calculating the yield of the product having the quality that passes the inspection by the inspection equipment from the probability of passing the inspection;
  • the production condition optimization device according to (1) for estimating
  • a regression model generation unit is further provided, and the regression model generation unit generates the above-mentioned A regression model is generated for estimating the amount of change in the quality of the product from the amount of change in the manufacturing conditions, and the quality change amount estimating unit uses the regression model to estimate the amount of change in the quality of the product from the amount of change in the manufacturing conditions.
  • the manufacturing condition optimization device according to (2) for estimating
  • the optimization processing unit maximizes the yield using at least one of an optimization algorithm that does not use derivatives, a local search method, a simulated annealing method, a tabu search method, and a genetic algorithm.
  • the production condition optimization apparatus according to (1) which calculates the amount of change in the production conditions.
  • the yield estimating unit when estimating the probability that the product will pass the inspection by the inspection facility from the amount of change in quality, based on the quality of a predetermined number of products manufactured most recently,
  • the production condition optimization device according to (2) which estimates the probability that the product will pass the inspection by the inspection equipment.
  • the yield estimating unit obtains a probability distribution of the quality of the product from the amount of change in the quality of the product, and estimates the probability that the product passes the inspection by the inspection equipment based on the probability distribution ( 2) The production condition optimization device described in 2).
  • a method for optimizing manufacturing conditions for a manufacturing condition optimizing apparatus comprising a step of estimating the yield of a product having a quality that passes an inspection by an inspection equipment when the manufacturing conditions for the product by the manufacturing equipment are changed. and calculating the amount of change in the manufacturing conditions that maximizes the yield.
  • the present invention it is possible to provide a manufacturing condition optimization device, a program, and a manufacturing condition optimization method that enable improvement of the manufacturing index by adjusting the manufacturing conditions of products even when there is disturbance.
  • FIG. 1 is an overall configuration diagram of a manufacturing system according to an embodiment; FIG. It is a figure for demonstrating the process outline
  • 1 is a functional block diagram of a manufacturing condition optimization device according to this embodiment;
  • FIG. 4 is a data configuration diagram of learning data according to the embodiment;
  • FIG. 4 is a data configuration diagram of manufacturing control data according to the embodiment;
  • FIG. 4 is a flowchart of regression model generation processing according to the present embodiment; 4 is a flowchart of objective function processing executed by an objective function processing unit according to the embodiment; 4 is a flowchart of optimization processing according to the embodiment;
  • FIG. 1 is an overall configuration diagram of a manufacturing system 10 according to this embodiment.
  • the manufacturing system 10 includes a manufacturing facility 410 , an inspection facility 420 , a manufacturing control device 430 and a manufacturing condition optimization device 100 .
  • Manufacturing equipment 410 , inspection equipment 420 , manufacturing control device 430 , and manufacturing condition optimization device 100 can communicate with each other via network 499 .
  • the manufacturing equipment 410 manufactures the products 460 under the manufacturing conditions set by the manufacturing control device 430 .
  • Manufacturing facility 410 transmits individual manufacturing conditions for product 460 to manufacturing control device 430 .
  • Product 460 is transported to inspection facility 420 for inspection.
  • a product 470 that has passed the inspection is shipped as a non-defective product.
  • Products 480 that fail inspection are defective and discarded.
  • the inspection facility 420 measures one or more qualities (quality items, inspection items) of each product 460, and if all the measured values are within the respective criteria (between the upper limit and the lower limit), The product shall pass.
  • the inspection facility 420 transmits the quality measurement value (simply referred to as quality or quality value) of each product 460 to the manufacturing control device 430 .
  • the manufacturing control device 430 stores manufacturing control data 440 (see FIG. 5 described later) that stores individual manufacturing conditions and quality of the product 460 .
  • the manufacturing condition optimization device 100 obtains manufacturing conditions that maximize the yield of the product 460 based on the manufacturing control data 440 . This makes it possible to improve the manufacturing index.
  • FIG. 2 is a diagram for explaining the outline of processing of the manufacturing condition optimization device 100 according to this embodiment.
  • a regression model 150 is generated from learning data 140 .
  • the learning data 140 is a set of the amount of change in the manufacturing conditions in the manufacturing equipment 410 (see FIG. 1) and the amount of change in the quality of the product 460 in the inspection equipment 420 when the manufacturing conditions are changed by the amount of change. data.
  • the regression model 150 is a model for estimating the amount of change in the quality of the product 460 (the amount of quality change) from the amount of change in the manufacturing conditions (the amount of change in the manufacturing conditions), and is, for example, a linear regression model.
  • the manufacturing condition optimization apparatus 100 uses the amount of change in the manufacturing conditions as an input (variable) and uses a yield estimation function for estimating the yield of the product 460 as the objective function to solve the optimization problem of maximizing the yield.
  • the yield estimation function uses the regression model 150 to calculate an estimated value of quality change from the input manufacturing condition change, obtains an estimated quality from the quality change, and estimates the yield.
  • the solution to this optimization problem is the variation in manufacturing conditions that maximizes yield.
  • the manufacturing condition optimization device 100 transmits this manufacturing condition change amount to the manufacturing management device 430 .
  • the manufacturing control device 430 can maximize the yield of the product 460 by setting the manufacturing conditions of the manufacturing equipment 410 according to the amount of change in the manufacturing conditions.
  • FIG. 3 is a functional block diagram of the manufacturing condition optimization device 100 according to this embodiment.
  • Manufacturing condition optimization apparatus 100 includes control unit 110 , storage unit 130 , communication unit 170 , and input/output unit 180 .
  • Communication unit 170 transmits and receives communication data to and from other devices including manufacturing control device 430 .
  • User interface devices such as a display, a keyboard, and a mouse are connected to the input/output unit 180 .
  • the storage unit 130 is composed of ROM (Read Only Memory), RAM (Random Access Memory), SSD (Solid State Drive), and the like.
  • the storage unit 130 stores a program 131 , learning data 140 (see FIG. 4 described later), and a regression model 150 .
  • the program 131 describes procedures for a regression model generation process (see FIG. 6 described later), an optimization process, and an objective function (yield estimation function) process (see FIG. 7 described later).
  • FIG. 4 is a data configuration diagram of the learning data 140 according to this embodiment.
  • the learning data 140 is, for example, tabular data, and one row (record) includes columns (attributes) of manufacturing condition variation 141 and quality variation 142 .
  • the manufacturing condition change amount 141 indicates the amount of change when the manufacturing conditions in the manufacturing facility 410 are changed.
  • the manufacturing conditions include one or more items, and the manufacturing condition variation 141 indicates the variation of the one or more items.
  • the quality change amount 142 indicates the quality change amount of the product 460 in the inspection equipment 420 when the manufacturing conditions indicated by the manufacturing condition change amount 141 are changed.
  • Quality variation 142 includes variation in one or more qualities (inspection measurements, quality values) for one or more products 460 .
  • control unit 110 includes a CPU (Central Processing Unit), and includes a learning data generation unit 111, a regression model generation unit 112, an optimization processing unit 113, an objective function processing unit 114, and a quality variation estimation unit. It includes a unit 115 , a quality estimator 116 and a yield estimator 117 .
  • the learning data generator 111 acquires manufacturing control data 440 (see FIG. 5 described later) stored in the manufacturing control device 430 and generates learning data 140 (see FIG. 4).
  • FIG. 5 is a data configuration diagram of the manufacturing control data 440 according to this embodiment.
  • the manufacturing control data 440 is, for example, tabular data, and one row (record) includes product identification information 441 (described as product ID (identifier) in FIG. 5), manufacturing date and time 442, manufacturing conditions 443, and quality 444. , and columns (attributes) of inspection results 445 .
  • the product identification information 441 is information for identifying each product 460, such as a serial number.
  • the date and time of manufacture 442 is the date and time when the product 460 was manufactured.
  • the manufacturing conditions 443 are the manufacturing conditions in the manufacturing equipment 410 when the product 460 is manufactured.
  • Quality 444 is a measure of inspection of product 460 by inspection facility 420 .
  • the inspection result 445 indicates pass/fail (OK/NG), which is the inspection result of the product.
  • the learning data generation unit 111 acquires the manufacturing control data 440 and divides the products into groups of products that are continuously manufactured and have the same manufacturing conditions 443 . Next, the learning data generator 111 obtains the amount of change in the manufacturing conditions 443 from the immediately preceding group for each group, and uses this as the amount of manufacturing condition change. Subsequently, for each group, the learning data generation unit 111 obtains the difference between the quality 444 of each product in the group and the average value of the quality 444 in the immediately preceding group, and uses it as a quality change amount. The learning data generation unit 111 adds a record to the learning data 140 (see FIG. 4), and sets the manufacturing condition variation 141 and the quality variation 142 as the obtained manufacturing condition variation and quality variation.
  • the regression model generation unit 112 generates a regression model 150 based on the learning data 140.
  • the learning data 140 is data indicating the amount of change in product quality (quality change amount 142) when the manufacturing conditions are changed by the manufacturing condition change amount 141.
  • the regression model 150 is a model referred to when estimating (calculating) the quality variation from the manufacturing condition variation, and is, for example, a linear regression model.
  • the regression model 150 is calculated as ⁇ shown by the following equation (1).
  • X is a matrix indicating manufacturing conditions referred to when generating the learning data 140 .
  • ⁇ X is a matrix indicating the amount of change in manufacturing conditions included in the learning data 140 .
  • ⁇ X T denotes the transposed matrix of ⁇ X.
  • ⁇ y is a vector indicating one quality item (inspection measurement value) of the product included in the learning data 140 . There are ⁇ y as many as the number of quality items (N, which will be described later).
  • ⁇ is the estimate of the partial regression coefficient, and there are as many quality items as there are.
  • the optimization processing unit 113 solves the optimization problem that maximizes the yield score represented by the equation (2) described later, using the yield estimation function described later as the objective function, and determines the change in manufacturing conditions that maximizes the yield. Calculate quantity.
  • Methods for solving optimization problems include optimization algorithms that do not use derivatives, local search methods, simulated annealing methods, tabu search methods, and genetic algorithms.
  • N is the number of qualities (quality items). i is the subscript of the quality item, and there are the 1st quality item to the Nth quality item.
  • M is the number of products whose yield is to be estimated, for example, the number of products manufactured during a predetermined period in the past. The optimization problem in this embodiment is to obtain the amount of change in manufacturing conditions that maximizes the number of products that pass the inspection among the M products to be manufactured.
  • j is the index of the M products to be manufactured, from the 1st product to the Mth product.
  • l L,i is the lower bound of the i-th quality item and the lower bound of the acceptance range of the i-th quality item.
  • Equation (3) Equation (3)
  • N( ⁇ , ⁇ 2 ) in Equation (3) represents a normal distribution with mean ⁇ and variance ⁇ 2 .
  • ⁇ y i is an estimated value of the average amount of change related to the i-th quality item, and is calculated by Equation (4) described later.
  • y 0,i,j is the measured value of the i-th quality item in the j-th product among the M products manufactured most recently.
  • SE( ⁇ y i ) is the standard error of the estimated value of the amount of change related to the i-th quality item, and is calculated by Equation (5) below.
  • s i is the standard error of regression for the i-th quality item, and is calculated by Equation (6) below.
  • Equation (4) is a vector indicating the amount of change in manufacturing conditions.
  • ⁇ is an estimated value of the partial regression coefficient of the learned linear model, and is calculated by Equation (1) described above.
  • M 0 is the number of products when ⁇ was calculated.
  • X is a matrix indicating manufacturing conditions referred to when generating the learning data 140 .
  • X T denotes the transposed matrix of X.
  • ⁇ x T denotes the transposed vector of ⁇ x.
  • K is the number of manufacturing conditions.
  • RSS is the residual sum of squares.
  • the objective function processing unit 114 estimates (calculates) the yield from the production condition change amount as the yield estimation function, which is the objective function. Specifically, the objective function processing unit 114 obtains the probability distribution of the quality of the product 460 shown in Equation (3) from the amount of change in the quality of the product 460, and based on this probability distribution, the product 460 is inspected by the inspection equipment 420. Estimate the probability of passing The objective function processing unit 114 estimates the yield using a quality variation estimation unit 115, a quality estimation unit 116, and a yield estimation unit 117, which will be described later.
  • the quality change amount estimator 115 calculates the quality change amount of the product 460 manufactured by the manufacturing equipment 410 from the manufacturing condition change amount of the manufacturing equipment 410 using the regression model 150 . Specifically, the quality change amount estimator 115 calculates the estimated value ⁇ yi of the quality change amount from the manufacturing condition change amount ⁇ x using Equation (4). Note that ⁇ in Equation (4) is ⁇ corresponding to the i-th quality item among N ⁇ s.
  • the quality estimator 116 obtains the distribution of yi,j , which is the predicted value of the i-th quality item in the j-th product. Specifically, the sum of the estimated value ⁇ y i of the quality change amount and the measured value y 0,i,j of the i-th quality item in the j-th product among the M products manufactured most recently is taken as the distribution Calculated as the average value of The variance of the distribution is obtained from the standard error SE( ⁇ y i ) of the estimated value ⁇ y i of the quality variation (see equation (5)) and the standard error of regression s i (see equation (6)). Let the obtained mean value and the normal distribution of the variance be the distribution of yi,j (see equation (3)).
  • the yield estimating unit 117 obtains the probability P (l L,i ⁇ y i,j ⁇ l U,i ) that y i,j satisfies the inspection criteria from the distribution of y i ,j , yield score ( (2)) is calculated.
  • objective function processing section 114 estimates the yield using quality variation estimation section 115 , quality estimation section 116 , and yield estimation section 117 .
  • FIG. 6 is a flowchart of regression model generation processing according to this embodiment.
  • the regression model generation process is executed at a predetermined timing, such as after a predetermined number of products have been inspected after a predetermined period or manufacturing conditions have been changed.
  • the learning data generator 111 acquires the manufacturing control data 440 (see FIG. 5) from the manufacturing control device 430 (see FIG. 1).
  • the learning data generator 111 generates learning data 140 (see FIG. 4 ) from the acquired manufacturing control data 440 .
  • the regression model generator 112 generates the regression model 150 (see formula (1)) from the learning data 140 .
  • FIG. 7 is a flowchart of objective function processing executed by the objective function processing unit 114 according to this embodiment.
  • the objective function processing is called and executed at necessary timing when the optimization processing unit 113 solves the optimization problem (see step S31 in FIG. 8, which will be described later).
  • step S21 the objective function processing unit 114 starts a process of repeating steps S22 to S26 for each of M products whose yield is estimated.
  • step S22 the objective function processing unit 114 starts a process of repeating steps S23 to S25 for each of N qualities (quality items).
  • step S ⁇ b>23 the quality variation estimation unit 115 uses the regression model 150 to calculate the quality variation from the manufacturing condition variation. Specifically, the quality change amount estimator 115 calculates the estimated value ⁇ yi of the quality change amount from the manufacturing condition change amount ⁇ x using Equation (4). In step S24, the quality estimation unit 116 obtains the distribution of y i,j (see equation (3)), which is the predicted value of quality (quality item).
  • step S25 the yield estimation unit 117 obtains the probability P (l L,i ⁇ y i,j ⁇ l U,i ) that y i, j satisfies the inspection criteria from the distribution of y i,j .
  • ⁇ i 1, ..., N P (l L, i ⁇ y i, j ⁇ l U, i )
  • ⁇ i 1, ..., N log (P ( l L,i ⁇ y i,j ⁇ l U,i )).
  • step S27 the yield estimation unit 117 calculates yield score (see formula (2)).
  • FIG. 8 is a flowchart of optimization processing according to this embodiment.
  • the optimization process is executed at a predetermined timing, such as a predetermined cycle or after manufacturing a predetermined number of products.
  • the optimization processing unit 113 solves the optimization problem that maximizes the yield score (see formula (2)) using the yield estimation function as the objective function.
  • the optimization processing unit 113 transmits the manufacturing condition change amount, which is the optimum solution, to the manufacturing management device 430 .
  • the manufacturing control device 430 that has received the optimum solution instructs the manufacturing equipment 410 to change the manufacturing conditions by the manufacturing condition change amount.
  • the manufacturing condition optimizing apparatus 100 obtains the manufacturing condition change amount (manufacturing condition change amount) that maximizes the yield based on the measured value (y 0,i,j ) of the quality of the most recently manufactured product. Specifically, the production condition optimization device 100 obtains the amount of change in the production conditions that is the amount of change in quality that maximizes the yield. For quality caused by unmeasured or uncontrollable disturbances, current disturbances that are considered to be the same or little different from the most recent disturbances in order to change the manufacturing conditions based on the quality of the most recently manufactured product. Manufacturing conditions can be set to improve quality depending on the situation. Ultimately, the manufacturing conditions are adjusted according to the uncontrollable disturbance state, and it is possible to maximize the yield of the product and improve the manufacturing index.
  • Regression model 150 in the above embodiment is a linear regression model, but may be another model.
  • a Gaussian process regression model may be used instead of the (generalized) linear model.
  • it may be a machine learning model such as a neural network model.
  • a machine learning model for predicting the amount of change in quality from the amount of change in manufacturing conditions may be generated and used in the yield estimation function (objective function).
  • the regression model 150 is a model for estimating the amount of quality change from the manufacturing conditions and the amount of change from the manufacturing conditions.
  • the manufacturing condition optimization device 100 uses the product quality distribution (N( ⁇ y i + y 0 , i, j , SE( ⁇ y i ) 2 +s i 2 )) to estimate the yield.
  • the manufacturing condition optimization device 100 acquires the quality measurement value as the inspection result (see step S11 in FIG. 6). Instead of the measured value of quality (quality item, inspection item), it is also possible to acquire the inspection result represented by two values of pass or fail such as an external inspection or the number of defects in one product.
  • the regression model 150 is a logistic regression model in the case of inspection results represented by binary values, and a Poisson regression model in the case of the number of defects. Both logistic regression models and Poisson regression models are included in generalized linear models.
  • the objective function processing unit 114, the quality variation estimation unit 115, the quality estimation unit 116, and the yield estimation unit 117 are divided for convenience of explanation.
  • a yield estimator for example, the yield estimating unit uses a regression model to estimate the yield of the product 470 having a quality that passes the inspection by the inspection facility 420 when the manufacturing conditions of the product 460 by the manufacturing facility 410 are changed. good too.
  • the distribution of the quality predictor y i,j (see equation (3)) is a normal distribution, but it may be a binomial distribution or a Poisson distribution.

Abstract

Dispositif d'optimisation de conditions de production (100), comprenant : une unité d'estimation de rendement (117) qui estime le rendement de produit d'une qualité qui passerait une inspection par un équipement d'inspection lorsque des conditions pour produire un produit par un équipement de production varient ; et une unité de traitement d'optimisation (113) qui calcule combien faire varier les conditions de production pour obtenir un rendement maximal.
PCT/JP2021/039329 2021-01-19 2021-10-25 Dispositif d'optimisation de conditions de production, programme et procédé d'optimisation de conditions de production WO2022158066A1 (fr)

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Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH08305763A (ja) * 1995-04-28 1996-11-22 Nippon Steel Corp 生産計画作成方法
JP2012226511A (ja) * 2011-04-19 2012-11-15 Hitachi Ltd 歩留まり予測システムおよび歩留まり予測プログラム
JP2020086784A (ja) * 2018-11-21 2020-06-04 株式会社日立製作所 製造条件特定システムおよび方法
JP2020166749A (ja) * 2019-03-29 2020-10-08 株式会社カネカ 製造システム、情報処理方法、および製造方法

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH08305763A (ja) * 1995-04-28 1996-11-22 Nippon Steel Corp 生産計画作成方法
JP2012226511A (ja) * 2011-04-19 2012-11-15 Hitachi Ltd 歩留まり予測システムおよび歩留まり予測プログラム
JP2020086784A (ja) * 2018-11-21 2020-06-04 株式会社日立製作所 製造条件特定システムおよび方法
JP2020166749A (ja) * 2019-03-29 2020-10-08 株式会社カネカ 製造システム、情報処理方法、および製造方法

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